Spectral clustering has been known since the end of the 20th century and is developing quite fest. Despite the lack of a strong theoretical basis, this method gives very good empirical results on artificial and real data. In this paper, algorithm (in general form) of spectral clustering has been described along with the results of empirical simulations comparing spectral clustering with k-means, partition around medoids, Ward and complete link „traditional" methods. The simulations have been made on datasets with known cluster structure generated from multivariate normal distribution, on datasets with noisy variables and on processed real images data. (original abstract)